3807708: CF_Big Data Analytics

Description

• Introduction into "Big Data Analytics"
• Gathering Data with Python
• Cleaning Up Data with Python
• Interpreting Data with R
• Predictive Models with R

Learning Outcomes

• Repeat the fundamental concepts and definitions in the area of big data analytics
• Understand the benefits of using and interpreting large data sets derived from various data sources.
• Solve assignments, especially case studies in the area of smart - Identify relationships between different types of data.
• Describe data an build prediction models.
• Compare solutions with regard to their prediction accuracy.
• Evaluation and select suitable prediction models.

Lectures Method

• Lecture with interactive elements
• Team project work

Admission Requirements

• Basic programming skills
• Basic skills of descriptive statistics

Literature

Field, A. P., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage.
Chang, W. (2013). R graphics cookbook. Beijing: O'Reilly.
Downey, A. (2013): Think Python - How to Think Like a Computer Scientist. Green Tea Press.

Materials

Will be provided in class

Comments

Cross-faculty elective subject:
Notice the special Multi-stage allocation process.